Liquefied petroleum gas or biomass cooking and severe infant pneumonia

Background: Household air pollution exposure is a risk factor for severe pneumonia. The effect of replacing biomass cooking with liquefied petroleum gas (LPG) cookstoves on severe infant pneumonia is uncertain. Methods: We conducted a randomized controlled trial among 3,200 pregnant women aged 18-34 years and 9 to <20 weeks gestation in India, Guatemala, Peru, and Rwanda May 2018–September 2021. Pregnant women were randomized to unvented LPG stoves and fuel (intervention) or continued biomass fuel cooking (control). We monitored intervention adherence and measured 24-hour personal exposure to fine particulate matter (PM2.5) in pregnant women and their offspring. The trial had 4 primary outcomes; the primary outcome described in the present report was severe pneumonia in the first year of life, identified by facility surveillance or verbal autopsy of deaths. Results: We randomized 3,195 pregnant women who gave birth to 3,061 infants. High intervention uptake led to reduced PM2.5 personal exposures among children (intervention median 24.2 μg/m3 (interquartile range (IQR) 17.8, 36.4); control median 66.0 μg/m3 (IQR 35.2, 132.0). There were 175 severe pneumonia episodes identified during the first year of life, with an incidence rate of 5.67 (95% confidence interval (CI) 4.55, 7.07) and 6.06 (4.81, 7.62) cases per 100-child years in intervention and controls (incidence rate ratio 0.96 [98.75% CI, 0.64, 1.44; p=0.81]. No severe adverse events associated with the intervention were reported. Conclusions: There was no significant difference in severe pneumonia incidence among infants of women randomized to LPG compared to biomass-burning cookstoves.

. Verbal Autopsy -Output data dictionary -page 24 o Table S8.Verbal Autopsy -ICD-10 derived cause of death codes -page 26 o Table S9.Characteristics of severe pneumonia cases (primary outcome) by study group -page 27 o Table S10.Time to event analysis -secondary analysis -page 31 o Table S11.Primary pneumonia analysis accounting for COVID-19 period and child´s age -secondary analysis -page 32 o Table S12.Rwanda -secondary analysis -page 33 o Table S13.Adverse events -page 34 IV.

Indirect exposure assessment for children
Due to the difficulty in instrumenting small children with particulate matter (PM) measuring equipment, we utilized a validated time-activity and microenvironmental concentration exposure reconstruction method to estimate children's exposure to fine PM (PM2.5) and carbon monoxide (CO) 1 .The method relies on objective minute-byminute measurement of location using Bluetooth beacons (see below) and minute-byminute measurement of pollutant concentrations in commonly frequented environments.
Exposures are reconstructed by estimating a time-weighted average based on the pollutant concentrations in each environment in which participants spend time 2 .We attempted collection of three 24-hour PM2.5 and CO measurements for children in the first year of life (<3 months, ~6 months, and ~12 months).

Microenvironmental PM 2.5 and CO measurements
Microenvironmental PM2.5 concentrations were measured with Enhanced Children's MicroPEMs (ECM, RTI International, Durham, NC USA).ECMs measure PM2.5 concentrations every 10 seconds via light-scattering and collect integrated samples on 15 mm PTFE filters (Measurement Technology Laboratories) 2 .Gravimetrically-corrected nephelometric PM2.5 concentrations are used in infants' indirect PM2.5 exposure estimates.We used the Lascar EL-USB-300 (Lascar Electronics) to measure continuous CO concentrations at 1-minute intervals.The Lascar is a large pen-sized device with a sensing range between 0 and 300 ppm.Details on deployment and analyses of data from these instruments have been described previously 2,3 .Monitored microenvironments were the most commonly occupied rooms (i.e., the main living/sleeping areas, the kitchen), and mothers who consented to be a mobile microenvironment and wore an exposure monitor and beacon logger 2 .

Indirect exposure measurements
We used Bluetooth ® -based beacons for estimating in which microenvironments infants spent time.between a Beacon and a logger and is used to determine the infant's location 4 .
In each microenvironment, we co-located a logger with PM2.5 and CO monitors.At 5 minute intervals, we assigned location to the area with the strongest average RSSI from the two beacons worn by the infant 4 .Reconstructed personal PM2.

Meeting objectives:
The main objectives of the meeting were to (1) provide an update on HAPIN pneumonia progress, (2) present pneumonia data from PINS (Pneumonia International Network Surveillance) and HAPIN (Household Air Pollution International Network), (3) reevaluate pneumonia trial case definition and make recommendations with the input of external child pneumonia experts.

HAPIN trial pneumonia data:
The main HAPIN trial data was presented based on data through June 20, 2019.The intervention status of the cases remained concealed for this analysis, the meeting, and all investigators and meeting participants maintained blinding at all times.These data represented <1% of the total child-months follow-up for children <1 year old across all the International Research Centers (IRCs).The total number of severe pneumonia cases was 11 (8 Guatemala, 0 Peru, 1 Rwanda, 2 India).The observed incidence of severe pneumonia was 5.6 events/100 child-years.Assuming equal person time between the study arms and a relative risk of 0.67 the estimated control group incidence was 6.7 events/100 child-years (95%CI 3.1, 10.2).The a priori background incidence required to be adequately powered assuming a 33% intervention effect size is 6 events/100 child-years.Therefore, the trial's estimated control group incidence based on blinded data (6.7 events/100 child-years) was likely meeting the target incidence (6 events/100 child-years).Meeting participants observed from the trial data that the surveillance activities at Guatemala were identifying and screening more study children with acute illnesses.Participants further observed that the incidence of severe pneumonia was much higher in Guatemala relative to the other IRCs.

PINS (Pneumonia International Network Surveillance) data:
The stated purpose of the PINS network was to understand pneumonia incidence and severity at the four IRCs given this was unknown prior to the trial.The surveillance system was designed to help determine, in parallel to the main trial, whether the number of severe pneumonia cases in the IRC study areas would likely be sufficient or if additional recruitment or other adaptions will be necessary.All IRCs other than Guatemala completed the surveillance data collection retrospectively through medical chart extraction.Guatemala collected data prospectively, mainly through HAPIN study staff.All IRCs reported surveillance data from a period of at least 17 months, with Rwanda reporting data from 30 months.All the IRC PINS incidence estimates were lower than the 6 events/100 child-years background target rate, except for Guatemala.
Furthermore, all the IRC PINS incidence estimates were lower than the observed HAPIN trial data incidence.Consistent with the HAPIN trial data incidence, the Guatemala PINS incidence estimates were higher than the other IRCs.Lastly, the oxygen saturation variable was recorded in most medical charts, and it was concluded that extraction of this variable from the medical chart could be considered for inclusion in the trial data since IRCs were reporting challenges in obtaining this measurement when the child was receiving supplemental oxygen.

Analysis of Guatemala IRC:
Additional analyses were conducted to evaluate why Guatemala was observed to have a higher incidence of severe pneumonia and why surveillance activities were identifying higher numbers of acutely ill children.Analyses comparing the case characteristics of pneumonia cases in Guatemala, compared to the other IRCs, showed no substantive differences that clearly indicated possible systematic deviations from the trial protocol when screening and evaluating children for severe pneumonia.It was concluded that the trial surveillance in Guatemala, mainly due to 24 hours a day and 7 days per week staffing, was likely to be more comprehensive than currently administered at the other IRCs.It was also observed that Guatemala's trial incidence is comparable to other published incidence rates from children with pneumonia in Guatemala, and meeting participants concluded that it is likely that the incidence of severe pneumonia in Guatemala is simply higher than in the other IRCs.

Evaluation of potential revised pneumonia definitions using HAPIN data:
We evaluated amended pneumonia definitions since the current pneumonia case definition was inconsistent with the World Health Organization case definition of pneumonia, as well as other landmark child pneumonia studies (e.g., PERCH), and this may limit external validity and generalizability of the trial findings.Notably, the two main features required of the pneumonia case definition for this trial are severity, to optimize public health impact of the trial by targeting cases at greatest risk of mortality, and objective diagnosis, to prioritize specificity and limit inclusion of non-pneumonia cases.
Four amended case definitions were assessed.Compared to the current pneumonia case definition, analyses demonstrated that the WHO pneumonia case definition, when enriched by confirmatory imaging and physiologic oxygen saturation thresholds, increased case severity, provided an incidence of 12.4 events/100 child-years (95%CI 7.8, 17.0) and power of 0.89 assuming a relative risk of 0.67.This amended definition maintained 0.80 power at a relative risk of 0.73.The two key aspects of the case definition were improved using this amended definition, severity and objective diagnosis (through imaging and pulse oximetry).

Other options discussed:
Multiple other options were evaluated and presented to the meeting participants, including no protocol change, no amendment to the case definition but an increase in sample size (across all IRCs and also only in Guatemala), amend the case definition and increase sample size, a combination of the above but with revised surveillance, and amending the pneumonia endpoint to a secondary trial outcome.

Final external expert advisor recommendations:
As the current follow-up time was <1% the external expert advisors recommended the following: 1. Amend the case definition to be consistent with the current WHO severe pneumonia definition and conduct confirmatory imaging on non-hypoxemic cases 2. Revise the oxygen saturation threshold to physiologic levels (<92% at Guatemala, India, Rwanda; <86% at Peru) 3. Children can be considered to have severe hypoxemic pneumonia if observed or documented to be receiving mechanical ventilation, high flow nasal cannula oxygen, or non-invasive ventilation with CPAP or BiPAP.The oxygen saturation variable can be extracted from the medical chart and the same thresholds detailed in recommendation #2 applied for defining hypoxemia.Death attributed to pneumonia should be included in the case definition.

4.
Strengthen pneumonia surveillance at the Peru, India, and Rwanda IRCs using existing resources.

Verbal autopsy methodology
Purpose: The Household Air Pollution Intervention Network (HAPIN) Trial aimed to assess the impact of a liquefied petroleum gas (LPG) cooking stove and fuel intervention on health.
This was done in four countries: Guatemala, India, Peru and Rwanda.One of the health outcomes that were recorded was deaths amongst children under-five years.The purpose of this exercise is to determine which of these deaths died of probable pneumonia.

Methods:
The verbal autopsy data will be analyzed using three methods, to produce 3 different primary cause of death variables.The primary cause of death will be described for PCVA and CCVA methods, and the three binary variables for "probable pneumonia death" will be described using proportions by country and age group.

Physician Coded verbal autopsy (PCVA)
Two physicians, blinded to both study arms, each other's assessments and any existing cause of death classification were asked to independently review the open narrative and close questions from the VAs.To be eligible, the physicians needed to be actively working in the study country and not have been a member of the HAPIN study team.
They were asked to assign at least one cause of death (primary), and if they deem appropriate, could assign a secondary cause.In cases where the primary cause of death was discrepant between the two physicians, a third independent physician review was done.The third reviewer had access to the cause of death allocation and was asked to arbitrate between these -or they could assign a new and different primary cause of death.In cases where no consensus could be reached, the VA was classified as "99 Undetermined", in line with previously published PCVA protocols 5 .The codes for cause of death were the ICD-10 World Health Organisation 2016 list (Table S6).
A binary variable was generated with 1=probable pneumonia death and 0=unlikely pneumonia death, if either the primary or secondary cause of death was coded as "10.03Neonatal pneumonia" or "01.02Acute respiratory infection, including pneumonia".

Computer coded verbal autopsy (CCVA)
The closed questions from the verbal autopsies were analysed using the WHO's OpenVA platform 6 .This includes multiple automated coding algorithms, and the cause of death from the InterVA-5 algorithm will be presented.This algorithm is based on Bayesian statistics, which assign a probable fraction for each cause of death, based on pre-specified weights assigned to each sign and symptom reported.The variables from the HAPIN VA tool were mapped to the WHO's 2016 VA tool.
The algorithms require each country to be classified as high (>1%), low (0. an age and sex to process the data.The neonatal VAs did not contain child sex, and therefore this was imputed as Male for all neonatal deaths; a sensitivity analysis using Female was conducted to check if the distribution of deaths changed.
A binary variable was generated with 1=probable pneumonia death and 0=unlikely pneumonia death, if either the primary or secondary cause of death was coded as "10.03Neonatal pneumonia" or "01.02Acute respiratory infection, including pneumonia".

Symptom coded
Using the two questions from the closed questions in the WHO VA tool: "Did baby/child have any difficulty with breathing?"and "Did baby/child have cough?", a binary variable will be created for probable pneumonia, using the same approach as WHO's IMCI algorithm.A child with a cough and/or difficult breathing and either fast breathing or chest in-drawing were classified as "1=probable pneumonia death".
A summary of the clinician coding process is presented in Table S2, and the primary cause of death assignment from both the algorithm and clinicians in presented in Table S3.For probable pneumonia deaths, using the symptom coding approach resulted in the largest number of deaths defined as "probable pneumonia" (25%), while the InterVA-5 coded approach the fewest (14%) -Table S4.

Comment on agreement:
Overall, the physician agreement observed in this exercise is comparable to published evaluations of agreement in child VA coding.We found that for 35/61 (57%) of VAs, there was agreement in the primary cause of death assigned by two independent physicians -and this was consistent with agreement for the sub-group who were eventually classified as probable pneumonia deaths (58%, 7/12).When a third physician reviewed those without agreement, this increased to 50/61 (82%).We observed variation in agreement by study setting and by death type, with higher disagreement from Guatemala and Peru and in infants versus neonates.The cause with highest agreement was birth asphyxia (91%).Other studies have reported similar rates: 60% agreement in infectious causes 7 ; 65% agreement for neonates and 72% for infants 8 ; 51% of deaths without a cause assigned due to disagreement 9 .
For PCVA and CCVA agreement, this has been found to be poor in deaths amongst children and neonates, and for infectious deaths -with different methods to cause of death assignment performing better for some causes than others 10,11 .Therefore, the lack of agreement in this exercise is not unexpected.Overall, 4/12 (33%) physician coded probable pneumonia deaths were also classified as pneumonia deaths using the InterVA5 algorithm.This is low, but the HAPIN VA tool did not completely map to the WHO 2016 tool which the InterVA-5 Bayesian algorithm is based on, and likely resulted in poorer performance.

Special considerations related to: Sex and gender
Infant severe pneumonia affects girls and boys similarly. 12

Age
Incidence of severe pneumonia is much higher during the first year of life than during later childhood or adulthood. 12Race or ethnic group Latino, Black and Asian persons may be disproportionately more affected by severe pneumonia worldwide when compared to whites.

Geography
Infants in resource-poor settings of Africa, Asia and Latin America are disproportionally more affected by severe pneumonia than children in Europe, North America and Australasia.Areas of Latin America, Africa and Southeast Asia are the most heavily impacted by exposure to household air pollution. 13LRI incidence is higher in Guatemala than most other countries (173 per thousand per year) and is higher in Rwanda (113 per thousand per year) than it is in India (54 per thousand per year) and Peru (97 per thousand per year).Guatemala, Rwanda, and India have LRI death rates (3.8-4.8 per 1000 per year) similar to low-middle-income countries on average, whereas Peru has lower LRI incidence (1.4 per 100 per year) that is similar to the middle-income countries on average.

Other considerations
Household air pollution is estimated to cause approximately 423,000 LRI deaths per year, and 152,000 (36%) of these deaths occur during the first year of life. 12ithin regions and countries, populations most impacted by HAP often include indigenous populations and lowerincome populations. 13

Overall representativeness of this trial
We studied the infant age range, which has the greatest burden of severe pneumonia attributable to HAP.Pregnant women were enrolled from obstetric clinics or national registries of pregnant women, which are representative of the population of pregnant women in the rural settings under study.The relative incidence rates of severe pneumonia in this trial by site were generally consistent the relative incidence and mortality rates of LRI from the global burden of disease study.   1 Vaccination status two weeks before achieving case status.Pentavalent vaccine used was DTwP-HepB+Hib (liquid).Pneumococcal conjugate vaccine was unavailable in India; denominator for up-to-date pneumococcal conjugate vaccine status 84 for intervention and 84 for control participants. 2Weight-for-length z score <-3 or if a length measurement is unavailable then weightfor-age z score <-3.For <60-day olds either weight-for-length z score <-3 or weight-forage z score <-3 used. 3Average of measurements when multiple measurements available.

Figures o Figure S1 .
Primary pneumonia case ascertainment schema -page 35 o Figure S2.Subgroup analyses -page 36 o Figure S3.Episodes and incidence of severe pneumonia and health care visits by COVID-19 period -page 37 1-1%) or very low (<0.1%) in terms of HIV/AIDs and malaria mortality.The countries were classified based on 2019 Global Burden of Disease data as follows: Guatemala: HIV/AIDS = low, malaria = very low; Peru: HIV/AIDS = high, malaria = very low; India: HIV/AIDS = low, malaria = low; Rwanda: HIV/AIDS = high, malaria = high.The following months were classified as being the rainy season in each country: Guatemala: May-October; Peru: November-March; India: June-September; Rwanda: March-May.InterVA5 requires both

Figure S3 .
Figure S3.Episodes and incidence of severe pneumonia (Panel A) and health care visits (Panel B) by COVID-19 period

Table S2 . Background information on the broader population affected by household air pollution -page 18 o Table S3. Air pollution exposure measurements -page 19 o Table S4. Verbal Autopsy -Summary of physician coding process
-page 21. o

Table S5 . Verbal Autopsy -Primary cause of death, assigned from different methods
-page 22 o

Table S6 . Verbal Autopsy -Probable pneumonia deaths, assigned through different verbal autopsy analysis methods
-page 23 o Table The beacon system consists of Bluetooth emitters (Model O, Roximity Inc. 4enver, CO, USA or EM Microelectronic, La Tène, Neuchâtel, Switzerland) and bluetooth signal loggers (Berkeley Air Monitoring Group, Berkeley, CA, USA).The emitter (hereafter 'beacon') is a coin-size device that constantly emits signals4; infants wore two Beacons sewed into their clothing.The Beacon logger is a smartphone-sized device that receives and logs Bluetooth signals emitted from the Beacons 4 .The logger records the beacon's unique media access control (MAC) address and the received signal strength indicator (RSSI) every 20 seconds.RSSI is proportional to the distance

Table S3 . Air pollution exposure measurements
2Missing includes invalid samples that failed to pass quantitative quality checks, including samples with unacceptable flow rates, filter damage, and measurement durations outside of 24 ± 4 hours.2Trial-period masurements refer to post-randomization pollutant values, which are presented as the median of the average of household-level measures.Missing includes invalid samples that failed to pass quantitative quality checks, including samples with unacceptable flow rates, filter damage, and measurement durations outside of 24 ± 4 hours.

Table S7 . Verbal Autopsy -Output data dictionary A
single .csvfile contains all the cause of death codes (HAPIN_CoD_alldata.csv).